EP 11: Siadhal Magos, Co-founder and CEO - Metaview
James Mackey 0:00
Hi, and welcome to episode 11 of Talent Acquisition Trends and Strategy. Today we're joined by Siadhal Magos. Siadhal how are you today?
Siadhal Magos 0:19
I am doing very well. Thank you, James, how are you?
James Mackey 0:23
Good, good. We have some very exciting topics to discuss today. And before we actually jump into all of that, I was hoping that you could share with everybody a little bit about your background and what you're up to right now.
Siadhal Magos 0:34
Yeah, absolutely. I'm one of the co-founders & CEO at Metaview. My background has always been in product management, always internet companies, and been working on Metaview for the last four years.
Our mission, we want to power people's decisions with the truth, what we are as a leading platform for driving the quality of interviews at modern high-growth companies. So really focus on helping organizations level up the quality of their interviews. And our big thing came from my experiences, I was at Uber before co-founding, is that we saw that when you're hiring at scale it is basically impossible to keep interviews high quality and consistent. There are just too many different interviews run by too many different people in too many different GEOS for too many different roles for anyone to really stay on top of the overall quality of that machine.
And that results in obvious, really negative consequences, like missing out on the best people and hiring some of the people who aren't a good fit for your company. Those things are super painful. They really damage the business velocity when you make those mistakes, and they really damage the culture.
So what we do at MetaView is we aim to give companies visibility into what is actually happening in their interviews, i.e. we record and transcribe interviews at scale across the company, and then generate unique data and insights. And pretty importantly, training flows to help interviewers calibrate and improve. So all about leveling up those qualitative interviews using technology.
James Mackey 2:08
So is it to some extent, this call recording and creating data based on that as it in some regards, is similar to Gong? Like what Gong is doing for sales intelligence, what possibly your team is bringing to recruitment intelligence?
Siadhal Magos 2:25
Yes, that's a really fair analogy. There are definitely quite a lot of differences actually at the product at the sort of application level in terms of what features make sense and are different in those different use cases. And obviously, we're, as a company, really passionate about helping organizations build amazing teams. And this was a different outcome that we have in mind. But it's a fair analogy, given the context.
And actually, we were really quite increasingly like that analogy, because the reason sales conversations make a tonne of sense to record and coach around and measure is because there's a very clear outcome that you want, and there's an expected structure of that conversation. And so if you sort of taking a few steps back and think, Well, what are the conversations in business? Is there a desired outcome? And is there a certain expected structure? Like number one on the list, the most common, like, consistent compensation type that has characteristics like that is interviewing. So yes, very similar in that way.
James Mackey 3:26
Cool. What I would love to kind of learn more about is how this tool sits alongside the tech stack, with a potential applicant tracking system. Because before I dive into some of the specific topics that we want to talk about, in regards to optimizing the interview process to achieve better outcomes, it'd be helpful to know kind of the point of impact of your product. So it can kind of just help me get a better pulse of the point of impact prior to jumping into other specifics.
Siadhal Magos 3:52
Yeah, it can be the headline is, I think you use the freight how's it sitting alongside them? Yeah, definitive answer. It sits alongside them. So it's not a replacement for any of those tools. At the applicant tracking system is still the sort of system of record and often the piece of software our customers use for scheduling interviews, we are an application that sits on top of that, and on top of whatever video conferencing solution you use. So you also don't use Metaview to actually run the interview method, you just passively record the conversations that are already happening scheduled by your ATS and over zoom.
The reason that's the right approach is really important to us and frankly, to our customers to not just be a tool for recruiters. If you want to really understand what's happening in your hiring process you're trying to build a high-performing consistent, high-quality interview process. You can't just capture the recruiters' interviews, you want to capture the vertical slice you want to see the candidate from end to end all the way to the point of like the final interview that results in the decision. And to do that you then have like new stakeholders to consider which are every interview In the company, and these people are not going to learn a new tool just for running that type of a conversation, right? They're going to expect to use the same tools that we're using.
So yes, it's really important to play seamlessly with the existing stack. And we have a piece of software that records the calls. And then to view the transcript and to view the analytics and the sort of insights and the nudges that we provide. You go to message, view messages app, so it's separate to Greenhouse and that all separately ATS at that point.
James Mackey 5:30
Sure. So your product, is it ideal that integrates with the ATS? Because I know one of the challenges, there are so many different ATS, that that's probably from an integration perspective, can be quite challenging.
How does that work? I mean, there's obviously the big players and growth stage, like, as you mentioned, Greenhouse, which is actually my favorite. So there are a few front runners, but it's an incredibly fragmented space. So how do you get out working alongside so many different providers out there?
Siadhal Magos 6:06
Yeah, I mean, it's not anything, it's definitely like, it's a part of our roadmap to make sure we're on top of the various integrations we need that make sense for our target customer. And so we have a handful of direct integrations with various applicant tracking systems, we also have a couple of ways of integrating with skip, like, scheduling systems. So things like Calendly and other tools are often used for scheduling interviews. And then there's also the calendar, which is a source of truth for some people as well. So it's not actually only the applicant tracking system that I couldn't dress, it is, I would say, a highly valuable integration with a customer, but not required for a product to work.
James Mackey 6:47
Thank you. This is all just super helpful context. So now we can get into actionable things to help people get the best results. But it just helps me kind of understand the point of impact that you're coming from when you're working with growth stages and then market organizations.
The biggest question I have is, What have you learned from the data thus far? Right? Like, what are the biggest holes and challenges that you see growth stage organizations making? In the interview process? What are the biggest opportunities for improvement right?
Siadhal Magos 7:16
The biggest is a great question. And I think the reason it's a little bit I'll probably end up giving more than one. is just because there's a number of stakeholders, there's a number of users of our products. They're the end interviewers, the people who actually run the conversation, and what are their sort of benefits? What have we learned about how to sort of help them best? That is the hiring managers who are responsible for the final decisions and candidates and the same thing, like how do we help them. And then there's the talent acquisition team who sort of care about the health of the machine as a whole.
And you need to do work, you need to provide value to everyone in that value chain, otherwise, your products are not gonna, like experience the growth and expansion returns for customers at the moment. So yeah, there are a couple of answers.
I would say the number one thing that we've learned about that sort of more strategic talent acquisition level, is the importance of being able to make appropriate comparisons. So telling a super high growth, like say, a company that just raised a couple of 100 million dollars, let's say, growing over 100%, and headcount year on year, telling them that they hate overall, your interviews don't seem to be that consistent, that's like not a useful piece of information, great, we can get that piece of information now.
What's really useful is when you can tell me the specific pockets of my organization where I have inconsistency issues, where I have overburdened interviewers, or where I have highly experienced interviewers who are interviewing very differently from the next generation of interviewers within their department. And so you have no reason to believe you're going to have like a consistently great team and future in that world. So really being able to hone in on like the specific pockets of where there may be quality issues has been like a big realization, we sort of started in this world where we gave very aggregate level insights and analytics. And it definitely got people excited, got people leaning in. And it wasn't enough for people to start to generate their own hypotheses about maybe how they can change the interview process within their companies.
But where we're really seeing the magic is when we get down to this sort of much more granular level, it's almost like not a big data anymore, you're sort of really even if the end is smaller, you're giving people up to some someone a much richer insight with which to start to, you know, apply that judgment too. So that would be my I think the biggest learning from the sort of more strategic talent acquisition perspective.
On the interview and hiring manager side, obviously really is bringing that time to value down. So the interviewers tend to be interested in their own data from a self-reflection perspective. But of course, if I offer an interview as an interviewer, you get to "hey you spoke more than 60% of that interview. And that's more than you usually do with any other candidate, you might want to think about that and see what to do differently next time.
Or you ask very few questions in this interview versus, compared to other people who run this interview in your company, that's a really good self-reflection moment, for sure, like very powerful, but where it gets really, where we really sort of taking it to them, where you can really take it to the next level is actually due to the level of synthesis for them of what to do differently next time, like specifically, like, ask questions when I have this or say this during your introduction. And so we have this realization essentially that while some people within these hybrid organizations do end up being super nerdy about being great at hiring, and great at interviewing, that's obviously not the majority.
The majority want to be good at interviewing, but they also have a tonne of other meetings they need to get to and a tonne of other objectives they're working on for the rest of their working day. And they really want that synthesis for them. And people end up being much more open to like clear suggestions of what to do differently in interviews than we actually first expected. We thought that would be like a dodgy ground to go on to like telling people what to do. Actually, it's not the case. If you get the messaging right, people are very happy to be sort of coached essentially on what to do differently.
James Mackey 11:12
So, how would you go about it? Is this the data measuring success by the percentage of candidates that go through to the next round? So, for instance, or is it based on some kind of equality of higher metric? I mean, when the system is given the feedback saying, mention, you know, XYZ in your intro, or ask more questions, how is it determining what good looks like?
Siadhal Magos 11:40
Yeah, so two sources. One is our own proprietary framework and syllabus for what good looks like. So this is something that we have designed based on, frankly, the data we've gathered over the last couple of years being live with customers, alongside members of our team who sort of constructed this proprietary framework.
That's based on things like, what we've seen historically, in terms of a number of questions and types of questions that tend to result in a more efficient funnel further down the funnel, or types of questions and types of conversations that tend to result in a candidate accepting an offer when it gets extended. The things inside, you can only derive when you have sort of historic data, combined with our own sort of Metaview's own perspective on best practice and interviews, which is much more sort of, I guess, experience-driven than necessary data-driven in that, in that in that context. So that's one area of sorts where that knowledge comes from his black metal views, knowledge.
And then the other area is the customer's own. So if you think about any hybrid organization, we can come in as many of you and say, Hey, you're hiring wrong, a good interview looks like this, this, and this. And we'd be naive to be telling a high-performing successful organization that that's the case, really, what they will do is look at their organizations and say, hey, you know, James is a really awesome representative of our culture here. We know he's great at hiring because he's made great hires, we want more of our people to be like him.
And so there you are taking the organization's own, almost, you can think of it as user-generated training content and saying, Hey, we know James is great, we have organization decided James is a great interviewer, we want more people to be like James, we're going to train people based on James's historic interview recording. So you can literally say, Hey, this is James has identified this as one of his favorite interviews where he felt like he did a great job, the next generation of people that are going to start to hire for this role, you're going to listen to two of James's interviews before you get unleashed on real candidates. Super simple, super high-impact way to get more people aware of what great interviewing looks like, in your organization.
James Mackey 13:52
Yeah, I think so. That's, I think the key right, like being able to reference like hiring managers or recruiters that not only are getting the best conversion rates and offer acceptance rates but also looking at the quality of hire, right? You can look at, like a contingent agency recruiter that might have a really high offer acceptance rate, but they also might have a really shitty, like, fall off rate, right, where, you know, 10 to 20% of the candidates that they're placing are falling off for the first three months.
So I think the challenge with any kind of optimization tool, data tool for anything, when it comes to the interview process, or quality of hire, as a primary outcome is how do you ensure the conversions. Because it's like, recruiting is constantly, beckoning with one hand like, and then also kind of holding back with the other like, you're trying to get people interested, but then you're trying to protect the quality, in terms of getting the best fit individuals and I feel like that's a lot of the times the challenge and saying, Okay, how do we kind of optimize this leveraging technology and data is figuring out like the balance between those two things. At least in my opinion, right?
And I think a lot of the tech out there, they're attempting to solve this problem, like the velocity, and the quality of the process. But I think the key part, from my perspective, is being able to plug in, okay, who are the most effective hiring managers that have a really high quality of hire across the board for their team? Even if that isn't necessarily like? How are you defining quality of hire, how are they pulling that, but just even if it's more of just like an understanding within the organization, that they are managing a high performing team, and then being able to plug their kind of knowledge and process back into data to guide others, I feel like that's a really critical piece.
Siadhal Magos 15:43
Yes. And actually, you're sort of like, bringing me back to maybe another like key learning, which is being very open to it to get like the customer organizations input in the way that you described. It is unrealistic to think that right away, a piece of software is going to tell you, these your best interviews with sure these the people who make the best hires know, you're going to have institutional knowledge, you're gonna have some tribal knowledge that lets the organization is going to make a judgment based on and he's going to make a bet based on that judgment. And we like to think that you get the best results when the product in our case, Metaview is super adapted to that.
If a company, customer x says that, we want more people to interview like this person, because we believe that they are good, but they should be entitled to feed that into our products. And they do, it's not like, it's not like most of us this all-seeing AI that can, you know, knows better than the people in the organization. We're here to help folks in the organization, do the thing that they're trying to do, but better. We're not necessarily trying to change, like, completely change how they go about interviewing, it's like, give them much better iteration news and give them much better ability to have leverage and impact over the interview process.
Because right now, it's such an unmanageable beast that it tends to be quite the most by most organizations and be a little bit unloved. Even though if you're, you know, again, like a 500-person organization going to 1000 people, your people are going to spend 10s of 1000s, maybe hundreds of 1000s of hours a year interviewing to get there. And so to not sort of try and impose some degree of deliberateness over that process is pretty crazy.
James Mackey 17:28
So, from my perspective, and again, just like my point of impact working with growth-stage tech companies, not every company has those individuals that are performing at a high level when it comes to interviewing, right? I think and I don't obviously know your product very well, just based on what we've discussed on this call, but, I would assume that it's like, okay, if you have like a reference point of, okay, this is how we want this is what leads to results. Like, for instance, at SecureVision, our head of delivery, who oversees all of our recruiting team is an incredible interviewer, she is just so good at spotting top talent. And we've been able to just stack our team with A players right, as a result of how she goes about interviewing our team.
But on the other hand, there are a lot of companies out there that may not have that type of hiring manager or leader within the organization. And then it just becomes like, it's, I guess the data of your kind of an aggregate or understanding of best practices really kind of matters more. Because if it's just pulling data off conversion rates and offering acceptance rates, that doesn't necessarily drive quality of hire and ensuring that you're getting the best fit individuals in like, there has to be some kind of external data input driving best practices, because then it's just like, Okay, we might accelerate or push people through. But are we actually getting the best fit individuals that particularly for growth stage companies are really going to help us scale right versus just sustainable? We already have.
So can you talk to me a little bit more about that? So, let's say you go into a, you know, a 500-person growth stage tech company, and they just really don't have the expertise when it comes to hiring and interviewing, and they're seeing the low quality of hire, they're seeing higher turnover? This tool elevates the quality of hire for those organizations as well? And, tell me a little bit more about that.
Siadhal Magos 19:21
Yes absolutely. And you're right, that sort of relationship does exist. Our customers all tend to be of the same broad ilk that is sort of essentially competing for the same people. So you do often see a low-quality process and the numbers in terms of offer acceptance or speed with which you get hot candidates through the pipeline, whatever it might be. So they usually say that there's a problem here, but in the case where the numbers look good, it's just you're hiring people who are not as high performing as another massive company, still provides you with industry benchmarks.
So although you have your intra-company benchmarks, we will let you know how that compares to other companies on our platform anonymously. Of course, we don't know specifically which company is where, but we'll let you know what good looks like for other companies on our platform. So you always have the ability to sort of cross-reference it. Okay, fine, this person might be good relative to our other interviewers. But how are they actually performing, given how the conviction leads us to other companies, and you can still get that insight.
The second thing I'd say is, yeah, our product does it. But also our team does it. But we work very closely with our customers. To make sure that we are having the impact we needed, leveling up interviews. And that means members of our team work closely with the leadership of the organization to make sure we're interpreting the data correctly and making the changes to that process accordingly. And everything you can imagine in between because we'd love to, it's the best way to have an impact, but also because it means we learn a tonne, and our product is obviously developing all the time. And the best way to create really robust learning is to have real intimacy with the customer.
James Mackey 21:03
Yeah, I love it. And one of the other questions I have is just on the candidate's side. So are you going to be developing the data and the actionable feedback in relation to Okay, let's say somebody is interviewing a product manager, right?
And the way that the product manager is answering the questions, are you collecting data on that side, too, to provide hiring managers with insights into like, you know, the How well did the candidate do in the interview? As well as that? Is that an element that you're looking at as well? Or is that maybe a further down-the-road solution?
Siadhal Magos 21:38
We're not so focused on that, to be honest with you. Obviously, as part of the product, organizations have access to the transcript in the recording. So if people want to refresh their memory on what was said by the candidate, so they're not just making their sort of decisions based on guesswork or memory, or whatever it might be, then that option is there, just you know, it's a utility, it's a function of the product.
From an analysis perspective, I think there's a tonne that already focuses on candidate assessment, what we focus on is the quality of the process, and quality of conversation, quality of the interview, so all of our energy goes into that side of the equation.
James Mackey 22:17
Okay, understood. Are you seeing any trends when it comes to interviewing, hiring, something that trends that you're seeing in terms of, again, you could answer this a few different ways, like, not necessarily in relation to your product, but just overall? When it comes to challenges you're seeing your customers face, or, maybe, the ninth percentile customers that are doing really, really well. And that's making the difference and allowing them to be successful. Just any trends that you can share with us on what you're seeing out there in the marketplace right now.
Siadhal Magos 22:51
Yes, there's one that is quite massive, your interview specific, and then there are others that are just broader product trends. I think the broader trend, partially inspired by this move to remote where, you know, the location of a company, for example, is irrelevant now. And the sort of how, how cushy and glamorous the office is, is irrelevant now as well, because at most, you're gonna spend a couple of days of the week there, you know, maybe not even that.
So a lot of those differentiators have gone away, which means you have to get creative about how you can differentiate, to attract people to work for you, or to identify and attract the right people to work for you.
One way to do that, a very expensive way to do that, is to pay more than everyone else, which obviously is the approach some companies take. But really, that's forcing companies to think, Okay, we can't really get a competitive advantage and how we source because, you know, everyone's got access to, everyone's online somewhere, and we all have the same tools at our disposal to try and find those people.
Where can we differentiate, one of those is in the interview, obviously, you know, who you actually meet, the human being you meet or human beings you meet during that interview process is the only thing that is different about your day, you're going to be sitting in the same spot looking at the same screen as you were before the interview. And after the interview. The only thing that's differentiated is who is the human being you spoke to, and what experiences you have.
But there are other things further upstream and downstream than that. We're seeing a lot of folks, I think, invest more and more in an employer brand, like how can we make someone have a perception about our company before they even spoke to one of the folks in the organization? Seeing a lot of companies invest a lot in, I guess, broadly, I would say, talent networks and talent intelligence, like actually understanding the market and having a pool ready to go to and being prepared to hire before you know before they need the button seats, basically.
So I think there are a few things where people are really starting to work out how they can differentiate in their process because a lot of the other differentiators have gone away. And I think that's one of the reasons for the Metaview. I think it works out well for a lot of customers, but there are other areas where that's true as well.
James Mackey 25:03
I mean, we're seeing a lot of those same similar chips right now two companies are fighting to figure out, Okay, how are we going to differentiate? And how are we going to stand out when we aren't seeing bigger emphasis and employer branding, similar to you? Just a much bigger emphasis on quality experience, I think.
I would love to see more talent acquisition functions, and just more companies in general, be a little bit more operationally driven. When it comes to talent acquisition, I think, you know, we're still seeing, you know, you look at revenue organizations, and you see these, like, large robust tech stacks, and companies are willing to invest, millions of dollars a year in and then you talk about, like budget for talent acquisition, and it's so much less, and it's okay, but you know, at a high level.
Siadhal Magos 25:49
You say that and I agree. The budget for talent acquisition might be less. But of course, if you fit the mental model, the budget for talent is huge. It's the majority of what we spend cash on is the people they hire. So it is still like a complete truth for these organizations that they know, who they can bring in is who they can retain, bringing and retaining is existential.
I think the thing, the shift that we're seeing, we're a part of it. But again, there are other parts of it that are the reason that you will see these more. The increase, essentially, talent acquisition, having a bigger seat at the table strategically, potentially having bigger budgets, just dedicated to the acquisition of new talent, is because now there can, it's actually a measurable part of is that it's not measurable. There used to be that every other part of the company was measurable, apart from how you hired people that's changing.
And when you start to have data images, you start to frankly, have more attention, you have more accountability, you have better sort of transparency into who's actually doing a great job and who's not. And so suddenly, you get this flywheel of increasing performance levels within that function. That's what I'd say at this stage, that's more of a, probably more of a theory than it is a fact I think it's like a slow, it's something that will take a while. But I see no reason why that won't be true, right? There's gonna be we're gonna have a school, the school board is pretty clear. And that's gonna bring out better things.
James Mackey 27:17
Well, I think it's a difference, philosophically, or maybe strategically, you could say, in some organizations that they still see, talent acquisition is more of a transactional, not motion where they're going through a big hiring surge. Okay, now, we have to increase the budget, because we're behind on talent acquisition goals, versus organizations that are truly getting the best outcomes, they're being a little bit more strategic and thinking long-term about talent acquisition, and they're open to making those the right investments.
Because they understand, even if hiring does turn out to be somewhat cyclical for my organization, I understand by laying the right foundation and getting the right tech in place, the right process in place, and optimizing that I'm ultimately going to be able to get the best fit individuals on my team on a consistent basis. So it's so common, still see a split between those two kinds of modes of thinking, right?
Fortunately, we've been able to kind of tailor our pitch and our online positioning to try to avoid the companies that are still looking at talent acquisition and transactions in a transactional way. And, you know, a few years ago, we had a lot of customers that quite honestly, we probably didn't see exactly eye to eye with, we just kind of adjusted our model to the point where our model doesn't even necessarily appeal to companies that view it that way anymore. Right? Like, we've just gone so strategic and embedded in terms of the solution that we're providing. And we're obviously on the services side, but for us, it's we've really just kind of gone all in with working with organizations that are committed to doing this the wrong way. They're thinking long term that is using and leveraging data, tech, and process to optimize.
So yes I think we're seeing more of it now, which is great. And, you know, we'll see if it continues to trend in that direction. We'll continue to see more data and better tech. I think I'm going that way. I just hope it's adopted at a large scale with the vast majority of tech organizations, that's still a part that I feel like, is kind of up in the air. Like are we going to see that level of adoption to data and tech that we see in revenue functions? Are we going? Are we going to get there? And it's I think we will fight to some extent, but that's what I'm really curious to see play out over the next few years.
Siadhal Magos 29:19
Yeah, I think we see analogies in our customer acquisition where it's a little maybe a little bit different to your business, but correct me if I'm wrong, obviously, where of course Mataview being a software product means it can be sort of applied to customers of different that maybe have different philosophies, different strategies or different objectives even. But we definitely have our roadmap aligned and we have the most success with customers who have made the bet that high-quality interviewing is important.
And there are other customers sometimes we win or work with where they're more interested in sort of like keeping an eye on things. And that's a different outlook. As I say, we're more on the former side, and we get more success, we get more excited. And, we do still have some decisions to make about customers we take on and it's not that all customers are not created equal, necessarily we have something really exciting to work with. And it's usually in that direction where we get most excited.
James Mackey 30:23
Yes, for sure. Well, look, I know, we're kind of coming up on time here, this has been a really interesting conversation, I think what you're doing is really important. And I think that you will continue to be successful and continue to grow. Because we do need to see a lot more of this in talent acquisition.
And I hope that the trends that we're referring to, I hope they turn out to be correct, as we start to see a larger scale technology and data adoption, within talent acquisition, and we start to see more of that budget focus specifically on the talent acquisition function and commitment to doing things the right way. I mean, you know, again, you ask most C-level executives, they're going to tell you that to some extent, they believe people are the primary driver of value.
But then when you look at like, Okay, how's that reflected in terms of, you know, the robustness of our, our leadership team and the robustness of the budget and the tech stack, and the focus that goes into ensuring that okay if people are actually the primary driver of value, is that reflected in how we're optimizing this organization? And in our priorities? And so I think there's still like, to some extent, a disconnect there.
But yes, I think that we are hopefully moving in the right direction because it's important, we have to do it. And the companies that are I'm sure, I'd be really interested to see, like, stat set side by side of okay, you before customers worked with you? And then, you know, after like, one year, I don't know, do you have any stats like that you can share with us as kind of a last topic of discussion?
Siadhal Magos 31:45
Yes, we do. And the most important metric is the quality of hire. But obviously, that's an incredibly hard thing to measure. And which is actually, I would argue, the reason why you see the disparity between, say, sales or revenue tuning and HR, because like, what's the output, what's the sort of the undeniably good thing we're aiming for? Here, it's not as clear on the downside, so the things that we focus on are a little bit more upstream, and represent representations of great interviewing.
Offer acceptance rate is one so we have before and after a 15% increase in offer acceptance rate among organizations that adopt Metaview, speed of hire. So candidates get through the departments that make use of Metaview candidates get through the pipeline, 28% quicker before and after a year after the adoption of Metaview.
And then the last one, which is a little bit softer, to be honest, but in many ways is the one we watch most closely. Because it's such a direct correlation to the impact we're having is the hiring manager and interviewer sentiment. So every time somebody engages with our product, we often hate that. Does it help you run better interviews, 91% of interviewers say that it does. And that's actually even a lot of time what our customers value most is, hey, if our interviewers and our arguments are telling us that this is helping them do this thing, then, that's good enough for us. Right?
James Mackey 33:09
You know what, one thing I was just thinking about too, and I don't know if this is something that comes up in your sales process, but people are leaving jobs faster than ever before, right? So turnovers are higher. And so knowledge transfer was becoming a real problem for a lot of organizations.
So I was thinking about this also could be a good solution to ensure that there's a proper knowledge transfer, let's say you have a leader or one of your best recruiters leaves the organization, you can basically more easily transfer that knowledge to new leaders and recruiters coming in which right now, it's not like 10 years are gonna be getting any longer anytime soon. Could be a really good way to ensure consistency as there is turnover within the organization.
Is that something that ever comes up in conversation in the sales process?
Siadhal Magos 33:55
It actually specifically doesn't, but I think it makes a tonne of sense. And I can easily imagine that. Frankly, being something we talk about more now because it's an awesome idea. We've been live with customers for two years. So obviously, there's a lot of our growth that has been recent, right? So a lot of the majority of our customer base actually we've been in the seat for nine, six months on this. So some of those sort of like, that churn related behavior we haven't seen a tonne of in a lot of our customers. But, I think it makes perfect sense what you just said.
James Mackey 34:32
Yeah, well, this has been a lot of fun. I could probably keep talking with you about this for hours to come. So I think we're gonna have to make sure that you can come back sometime soon.
Thank you so much for having this conversation. And joining us today. This was so incredibly valuable. And you really have some interesting fresh perspectives that you're bringing to the table. So it's been an absolute pleasure speaking with you today.
Siadhal Magos 34:52
Yeah, right back at you. I really enjoyed it. Cheers James!
James Mackey 34:55
Cool. And for everybody else, thanks for joining and we'll talk to you next time.